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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Li, Zhuo Shen, Yifei Xu, Ruiqing Yang, Yu Cao, Jiannong Wu, Linchun Wu, Qing School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Computer Science University College London London United Kingdom Donglian Information Technology Co. Ltd. Chengdu China Department of Pharmacy Children’s Hospital of Chongqing Medical University National Clinical Research Center for Child Health and Disorders Chongqing China

ISBN: (纸本)9783031306778

Epilepsy in childhood is a common neurological disorder in children. Most cases are benign childhood epilepsy, which can be controlled with medication by adaptive adjustment of the dosage of antiepileptic drugs (AEDs). Recently, reinforcement learning-based intelligent dosing has attracted increasing attention. In clinical practice, patients usually take more than one drug at a time, however, conventional reinforcement learning algorithms do not sufficiently address the combination of two or more active drugs. In this paper, we propose the multi-action offline reinforcement learning (MA-ORL) model to solve this problem. Concretely, MA-ORL inherits the basic framework of the actor-critic network. For the patient’s health and safety concerns, MA-ORL abandons the intrusive and high-risk "trial-and-error" interactions with the environment but directly learns from the offline clinical dataset in the initial phase until the model is sufficiently trained and ready to use. Besides, to choose multiple actions simultaneously, we replace the actor’s output in standard reinforcement learning with a 2D matrix indicating the mixed feature representation of all different actions, then multiply it with separate masks to obtain separated actions. In addition to reaching an optimal return (i.e., the reduction of seizure frequency), MA-ORL also emphasizes the accuracy of the recommended dosage. Therefore, we introduce weak supervision to the learning objective to restrict the range of the learning outcome. It guarantees the recommended dosage by MA-ORL is as close as the one prescribed by experienced physicians. We conduct extensive experiments on clinical medical records containing 245 cases of epilepsy in childhood. Experimental results show MA-ORL reaches the highest cumulative return among all baseline models. Moreover, the suggested amount of medication taken a day by MA-ORL is more accurate than any other benchmark. © 2023, The Author(s), under exclusive license to Springer Natu

关键词： Reinforcement learning

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Diabetes Type 2: Poincaré Data Preprocessing for Quantum Machine Learning

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computers, Materials & Continua 2021年第5期67卷 1849-1861页

作者： Daniel Sierra-Sosa Juan D.Arcila-Moreno Begonya Garcia-Zapirain Adel Elmaghraby Department of Computer Science and Engineering University of LouisvilleLouisvilleKYUSA Apolo Scientific Computing Center Universidad EAFITMedellínColombia eVida Research Group University of DeustoBilbaoSpain

Quantum Machine Learning(QML)techniques have been recently attracting massive *** reported applications usually employ synthetic or well-known *** of these techniques based on using a hybrid approach combining quantum and classic devices is the Variational Quantum Classifier(VQC),which development seems *** being largely studied,VQC implementations for“real-world”datasets are still challenging on Noisy Intermediate Scale Quantum devices(NISQ).In this paper we propose a preprocessing pipeline based on Stokes parameters for data *** pipeline enhances the prediction rates when applying VQC techniques,improving the feasibility of solving classification problems using NISQ *** including feature selection techniques and geometrical transformations,enhanced quantum state preparation is ***,a representation based on the Stokes parameters in the PoincaréSphere is possible for visualizing the *** results show that by using the proposed techniques we improve the classification score for the incidence of acute comorbid diseases in Type 2 Diabetes Mellitus *** used the implemented version of VQC available on IBM’s framework Qiskit,and obtained with two and three qubits an accuracy of 70%and 72%respectively.

关键词： Quantum machine learning data preprocessing stokes parameters Poincarésphere

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On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering

arXiv

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arXiv 2023年

作者： Trosten, Daniel J. Løkse, Sigurd Jenssen, Robert Kampffmeyer, Michael C. Department of Physics and Technology UiT The Arctic University of Norway Norway UiT Machine Learning group Visual Intelligence Centre Norway Norwegian Computing Center Norway Department of Computer Science University of Copenhagen Denmark Pioneer Centre for AI

Self-supervised learning is a central component in recent approaches to deep multi-view clustering (MVC). However, we find large variations in the development of self-supervision-based methods for deep MVC, potentially slowing the progress of the field. To address this, we present DeepMVC, a unified framework for deep MVC that includes many recent methods as instances. We leverage our framework to make key observations about the effect of self-supervision, and in particular, drawbacks of aligning representations with contrastive learning. Further, we prove that contrastive alignment can negatively influence cluster separability, and that this effect becomes worse when the number of views increases. Motivated by our findings, we develop several new DeepMVC instances with new forms of self-supervision. We conduct extensive experiments and find that (i) in line with our theoretical findings, contrastive alignments decreases performance on datasets with many views;(ii) all methods benefit from some form of self-supervision;and (iii) our new instances outperform previous methods on several datasets. Based on our results, we suggest several promising directions for future research. To enhance the openness of the field, we provide an open-source implementation of DeepMVC, including recent models and our new instances. Our implementation includes a consistent evaluation protocol, facilitating fair and accurate evaluation of methods and components1 Copyright © 2023, The Authors. All rights reserved.

关键词： Alignment

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Random Shortening of Linear Codes and Applications

arXiv

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arXiv 2023年

作者： Chen, Xue Cheng, Kuan Li, Xin Mao, Songtao School of Computer Science University of Science and Technology of China China Center on Frontiers of Computing Studies Peking University China Department of Computer Science Johns Hopkins University United States

Random linear codes (RLCs) are well known to have nice combinatorial properties and near-optimal parameters in many different settings. However, getting explicit constructions matching the parameters of RLCs is challenging, and RLCs are hard to decode efficiently. This motivated several previous works to study the problem of partially derandomizing RLCs, by applying certain operations to an explicit mother code. Among them, one of the most well studied operations is random puncturing, where a series of works culminated in the work of Guruswami and Mosheiff (FOCS’ 22), which showed that a random puncturing of a low-biased code is likely to possess almost all interesting local properties of RLCs. In this work, we provide an in-depth study of another, dual operation of random puncturing, known as random shortening, which can be viewed equivalently as random puncturing on the dual code. Our main results show that for any small Ε, by starting from a mother code with certain weaker conditions (e.g., having a large distance) and performing a random (or even pseudorandom) shortening, the new code is Ε-biased with high probability. Our results hold for any field size and yield a shortened code with constant rate. This can be viewed as a complement to random puncturing, and together, we can obtain codes with properties like RLCs from weaker initial conditions. Our proofs involve several non-trivial methods of estimating the weight distribution of codewords, which may be of independent interest. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal encoding

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TraceStream: Anomalous Service Localization based on Trace Stream Clustering with Online Feedback

TraceStream: Anomalous Service Localization based on Trace S...

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International Symposium on Software Reliability Engineering (ISSRE)

作者： Tong Zhou Chenxi Zhang Xin Peng Zhenghui Yan Pairui Li Jianming Liang Haibing Zheng Wujie Zheng Yuetang Deng School of Computer Science Fudan University China Shanghai Key Laboratory of Data Science Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China Tencent Inc. China

Modern large-scale service-based systems such as microservice systems have become increasingly complex, making it hard to localize anomalous services when various issues emerge. Traces record the workflows of requests through service instances and have been widely used in anomaly detection and root cause analysis. Existing trace-based approaches widely use statistical methods or learning-based techniques to detect trace anomalies and localize anomalous services. However, these approaches often suffer from the concept drift problem, i.e., the statistical properties of traces change over time in unforeseen ways. In this paper, we propose TraceStream, an anomalous service localization approach based on trace data stream clustering. TraceStream uses data stream clustering to discover potential anomalous trace clusters in evolving trace data and uses spectrum analysis to localize anomalous services based on the clusters. Moreover, TraceStream can effectively incorporate the online feedback of operation engineers based on the trace clusters to improve the accuracy for localizing anomalous services. Our evaluation confirms that TraceStream can effectively detect anomalies and localize anomalous services in an evolving microservice system. It can effectively incorporate human feedback to further improve the performance of anomalous service localization. Moreover, TraceStream is efficient and its efficiency can be further improved by sampling a small portion of traces by cluster.

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Neural P3M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs 38

Neural P3M: A Long-Range Interaction Modeling Enhancer for G...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Yusong Cheng, Chaoran Li, Shaoning Ren, Yuxuan Shao, Bin Liu, Ge Heng, Pheng-Ann Zheng, Nanning National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University China University of Illinois Urbana-Champaign United States Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong University of Science and Technology of China China Microsoft Research AI4Science United States

Geometric graph neural networks (GNNs) have emerged as powerful tools for modeling molecular geometry. However, they encounter limitations in effectively capturing long-range interactions in large molecular systems due to the localization assumption of GNN. To address this challenge, we introduce Neural P3M, a versatile enhancer of geometric GNNs to expand the scope of their capabilities by incorporating mesh points alongside atoms and reimaging traditional mathematical operations in a trainable manner. Neural P3M exhibits flexibility across a wide range of molecular systems and demonstrates remarkable accuracy in predicting energies and forces, outperforming on benchmarks such as the MD22 dataset. It also achieves an average improvement of 22% on the OE62 dataset while integrating with various architectures. Codes are available at https://***/OnlyLoveKFC/Neural_P3M. © 2024 Neural information processing systems foundation. All rights reserved.

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OneBit: Towards Extremely Low-bit Large Language Models

arXiv

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arXiv 2024年

作者： Xu, Yuzhuang Han, Xu Yang, Zonghan Wang, Shuo Zhu, Qingfu Liu, Zhiyuan Liu, Weidong Che, Wanxiang Research Center for Social Computing and Information Retrieval Harbin Institute of Technology Harbin China Department of Computer Science & Technology Tsinghua University Beijing China

Model quantification uses low bit-width values to represent the weight matrices of existing models to be quantized, which is a promising approach to reduce both storage and computational overheads of deploying highly anticipated LLMs. However, current quantization methods suffer severe performance degradation when the bit-width is extremely reduced, and thus focus on utilizing 4-bit or 8-bit values to quantize models. This paper boldly quantizes the weight matrices of LLMs to 1-bit, paving the way for the extremely low bit-width deployment of LLMs. For this target, we introduce a 1-bit model compressing framework named OneBit, including a novel 1-bit parameter representation method to better quantize LLMs as well as an effective parameter initialization method based on matrix decomposition to improve the convergence speed of the quantization framework. Sufficient experimental results indicate that OneBit achieves good performance (at least 81% of the non-quantized performance on LLaMA models) with robust training processes when only using 1-bit weight matrices. Code and checkpoints are available at https://***/xuyuzhuang11/OneBit © 2024, CC BY.

关键词： Matrix algebra

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A Blackbox Model Is All You Need to Breach Privacy: Smart Grid Forecasting Models as a Use Case*

arXiv

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arXiv 2023年

作者： Aly, Hussein Al-Ali, Abdulaziz Al-Ali, Abdullah Malluhi, Qutaibah Qatar University Collage of Engineering Department of Computer Science and Engineering Doha Qatar KINDI Center for Computing Research Qatar

This paper investigates the potential privacy risks associated with forecasting models, with specific emphasis on their application in the context of smart grids. While machine learning and deep learning algorithms offer valuable utility, concerns arise regarding their exposure of sensitive information. Previous studies have focused on classification models, overlooking risks associated with forecasting models. Deep learning based forecasting models, such as Long Short Term Memory (LSTM), play a crucial role in several applications including optimizing smart grid systems but also introduce privacy risks. Our study analyzes the ability of forecasting models to leak global properties and privacy threats in smart grid systems. We demonstrate that a black box access to an LSTM model can reveal a significant amount of information equivalent to having access to the data itself (with the difference being as low as 1% in Area Under the ROC Curve). This highlights the importance of protecting forecasting models at the same level as the data. Copyright © 2023, The Authors. All rights reserved.

关键词： Smart power grids

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Internet Representation of Research Institutions as a Result of science Reform

Internet Representation of Research Institutions as a Result...

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Viktor Medennikov Information and Computing Systems Department Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Russia

The paper considers the condition and comparison of representation on the Internet of both unaffected and reformed research institutions, in order to form a methodology for assessing the possibility of adequate and timely response to the enormous social and economic challenges occurring in the world; for this purpose, websites' content condition and dynamics are analyzed using the example of agricultural research institutions based on monitoring thereof in 2017 and 2022. It is shown that the science reform proved itself inconsistent since at first it did not respond to the demands of production during the pandemic, and now to the requests due to sanctions. The result of the science reform was a significant increase in the number of uninformative websites, which contradicts the new social order that all sectors of society, business, and management generate for digital tools for transferring scientific developments to the economy against the backdrop of severe sanctions and the pandemic.

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Optimal Logistics Activities Based Deep Learning Enabled Traffic Flow Prediction Model

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computers, Materials & Continua 2022年第12期73卷 5269-5282页

作者： Basim Aljabhan Mahmoud Ragab Sultanah M.Alshammari Abdullah S.Al-Malaise Al-Ghamdi Ports and Maritime Transportation Department Faculty of Maritime StudiesKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Center of Excellence in Smart Environment Research King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia S 7Information Systems Department HECI SchoolDar Alhekma UniversityJeddahSaudi Arabia

Traffic flow prediction becomes an essential process for intelligent transportation systems(ITS).Though traffic sensor devices are manually controllable,traffic flow data with distinct length,uneven sampling,and missing data finds challenging for effective *** traffic data has been considerably increased in recent times which cannot be handled by traditional mathematical *** recent developments of statistic and deep learning(DL)models pave a way for the effectual design of traffic flow prediction(TFP)*** this view,this study designs optimal attentionbased deep learning with statistical analysis for TFP(OADLSA-TFP)*** presentedOADLSA-TFP model intends to effectually forecast the level of traffic in the *** attain this,the OADLSA-TFP model employs attention-based bidirectional long short-term memory(ABLSTM)model for predicting traffic *** order to enhance the performance of the ABLSTM model,the hyperparameter optimization process is performed using artificial fish swarm algorithm(AFSA).A wide-ranging experimental analysis is carried out on benchmark dataset and the obtained values reported the enhancements of the OADLSA-TFP model over the recent approaches mean square error(MSE),root mean square error(RMSE),and mean absolute percentage error(MAPE)of 120.342%,10.970%,and 8.146%respectively.

关键词： Traffic flow prediction deep learning artificial fish swarm algorithm mass gatherings statistical analysis logistics

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